Abstract
Given a heterogeneous time-series sample, it is required to find the points in time (called change points) where the probability distribution generating the data has changed. The data is assumed to have been generated by arbitrary, unknown, stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework; the theoretical results are complemented with experimental evaluations.
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Khaleghi, A., Ryabko, D. (2013). Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series. In: Jain, S., Munos, R., Stephan, F., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2013. Lecture Notes in Computer Science(), vol 8139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40935-6_27
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DOI: https://doi.org/10.1007/978-3-642-40935-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40934-9
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